Max-min Fairness in 802.11 Mesh Networks

In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks. By exploiting features of the 802.11e/n MAC, in particular TXOP packet bursting, we are able to use this characterisation to establish a straightforward, practically implementable approach for achieving max-min throughput fairness. We demonstrate that this approach can be readily extended to encompass time-based fairness in multi-rate 802.11 mesh networks

Supermultiplexed optical imaging and barcoding with engineered polyynes

Optical multiplexing has a large impact in photonics, the life sciences and biomedicine. However, current technology is limited by a 'multiplexing ceiling' from existing optical materials. Here we engineered a class of polyyne-based materials for optical supermultiplexing. We achieved 20 distinct Raman frequencies, as 'Carbon rainbow', through rational engineering of conjugation length, bond-selective isotope doping and end-capping substitution of polyynes. With further probe functionalization, we demonstrated ten-color organelle imaging in individual living cells with high specificity, sensitivity and photostability. Moreover, we realized optical data storage and identification by combinatorial barcoding, yielding to our knowledge the largest number of distinct spectral barcodes to date. Therefore, these polyynes hold great promise in live-cell imaging and sorting as well as in high-throughput diagnostics and screening

Amyloid Precursor Protein Is Trafficked and Secreted via Synaptic Vesicles

A large body of evidence has implicated amyloid precursor protein (APP) and its proteolytic derivatives as key players in the physiological context of neuronal synaptogenesis and synapse maintenance, as well as in the pathology of Alzheimer's Disease (AD). Although APP processing and release are known to occur in response to neuronal stimulation, the exact mechanism by which APP reaches the neuronal surface is unclear. We now demonstrate that a small but relevant number of synaptic vesicles contain APP, which can be released during neuronal activity, and most likely represent the major exocytic pathway of APP. This novel finding leads us to propose a revised model of presynaptic APP trafficking that reconciles existing knowledge on APP with our present understanding of vesicular release and recycling

Providing fairness and maximising throughput in 802.11 wireless mesh network.

The thesis addresses two questions: how to provide fairness in 802.11 wireless mesh networks and how to maximize the overall throughput in an distributed way. Fairness and efficiency are two fundamental issues in wireless networks, which are widely researched in both wired and wireless networks. We consider 802.11 wireless networks due to their ubiquity and practical importance. The likely trend of the wireless networks is toward the use of multi-hop wireless networks (so-called mesh networks), which provide the potential of serving the users in larger converage with higher throughput. However, achieving fairness and efficiency remain bottleneck issues in the rollout of production-quickly mesh networks

Achieving fairness in lossy 802.11e wireless multi-hop mesh networks

We consider achieving max-min fairness in 802.11e based multi-hop wireless networks. We propose an approach which makes use of the TXOP mechanism in combination with an automatic contention window size tuning algorithm based onchannel state sensing. Simulation results show that the proposed approach can provide a good approximation to per-flow max-min fairness and that this is achieved regardless of the active number of flows and when the channel is noisy

Max-min Fairness in 802.11 Mesh Networks

In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks

Stochastic Optimal Dispatch of Virtual Power Plant considering Correlation of Distributed Generations

Virtual power plant (VPP) is an aggregation of multiple distributed generations, energy storage, and controllable loads. Affected by natural conditions, the uncontrollable distributed generations within VPP, such as wind and photovoltaic generations, are extremely random and relative. Considering the randomness and its correlation of uncontrollable distributed generations, this paper constructs the chance constraints stochastic optimal dispatch of VPP including stochastic variables and its random correlation. The probability distributions of independent wind and photovoltaic generations are described by empirical distribution functions, and their joint probability density model is established by Frank-copula function. And then, sample average approximation (SAA) is applied to convert the chance constrained stochastic optimization model into a deterministic optimization model. Simulation cases are calculated based on the AIMMS. Simulation results of this paper mathematic model are compared with the results of deterministic optimization model without stochastic variables and stochastic optimization considering stochastic variables but not random correlation. Furthermore, this paper analyzes how SAA sampling frequency and the confidence level influence the results of stochastic optimization. The numerical example results show the effectiveness of the stochastic optimal dispatch of VPP considering the randomness and its correlations of distributed generations